📅 2023-12-25 — Session: Developed Python Scripts for Financial Data Aggregation
🕒 14:40–16:05
🏷️ Labels: Python, Pandas, Data Aggregation, Financial Analysis, Error Handling
📂 Project: Accounting
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to develop and refine Python scripts for the aggregation and analysis of financial data, specifically focusing on expenses and debts.
Key Activities
- Data Formatting: Defined a specific format for expense data to facilitate analysis and grouping using Python.
- Data Aggregation: Developed Python scripts to group expenses by month and category, leveraging the Pandas library for data processing.
- Data Pivoting: Utilized Pandas to pivot DataFrames, consolidating categories into columns and aggregating values by year and month.
- Debt Accounting: Proposed a system for recording family debts and payments, transforming payments into debts and establishing a detailed recording system.
- Error Handling: Identified and addressed an error related to mismatched list lengths in a dictionary used for creating a Pandas DataFrame, ensuring uniformity before recalculating year-end balances.
- System Planning: Outlined a system for tracking financial flows related to car transactions, including purchases, sales, payments, and repairs.
Achievements
- Successfully created and tested Python scripts for financial data aggregation and pivoting.
- Developed a comprehensive approach for handling family debts and payments.
- Resolved data validation errors to ensure accurate financial calculations.
Pending Tasks
- Further refine the Python scripts to handle additional edge cases in data aggregation.
- Implement the proposed system for tracking car-related financial transactions.